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Vision-language models must reconcile conflicting text and images. Prior work has characterized the resulting behavior in terms of modality biases--e.g., a"blind faith in text"--but why preferences vary across models, domains, and input conditions remains unclear. We develop a predictive account from a Bayesian perspec...
Upperological Signatures is presented for characterizing the structure of embedding spaces and finds that it outperforms individual metrics in downstream performance prediction and reveals novel insights into representational similarity that challenge the global claims of the Platonic Representation Hypothesis.
This work introduces a framework for identifying whether a model's drug semantics are driven mainly by the affix, the stem, or the drug name as a whole, and reveals that models often induce drug meaning primarily through affix cues, yet rarely explicitly indicate this reliance, and sometimes incorrectly conflate proper...
Across various benchmarks, it is shown that the representations learned by Mol-JEPA deliver strong performance, demonstrating the value of incorporating biochemical context through latent space prediction.
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